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Predicting Gastric Cancer Response to Chemo With Multimodal AI Model

Recruitingn/aAges 20 Years - 90 YearsAllSixth Affiliated Hospital, Sun Yat-sen University
NCT06451393 on ClinicalTrials.gov ↗
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In plain words

What the trial is, and whether it could suit you

This study aims to develop a multimodal model combining radiomic and pathomic features to predict pathological complete response (pCR) in advanced gastric cancer patients undergoing neoadjuvant chemotherapy (NAC). The researchers intended to collected pre-intervention CT images and pathological slides from patients, extract radiomic and pathomic features, and build a prediction model using machine learning algorithms. The model will be validated using a separate cohort of patients. This research intend to build a radiomic-pathomic model that can outperform models based on either radiomic or pathomic features alone, aiming to improve the prediction of pCR in gastric cancer.

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📋 Eligibility rules (as published)
Inclusion Criteria: * patients with histologically confirmed adenocarcinoma of the stomach or esophagogastric junction who received NAC and radical gastrectomy; * patients who underwent abdominal multidetector computed tomography (CT) inspection, gastroscope, and tumor tissue biopsy before any intervention started; * Lesions that are assessable according to The Response Evaluation Criteria in Solid Tumors Version 1.1 Exclusion Criteria: * Patients with indistinguishable tumor lesions on the CT images due to insufficient filling of the stomach during the CT inspection; * patients without indistinguishable tumor cell on the pathological slides due to inadequate sampling; * patients with insufficient data.
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What it tests

  • Neoadjuvant chemotherapy with radical tumor resection surgery
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Where

1 site
The Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, China
🤝 Taking part is your choice, and you can leave at any time. Only the trial team can confirm you are eligible — always decide together with your own doctor.
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